An Analysis: wealth creation by the industrial companies listed on the Johannesburg stock exchange of South Africa, 2005 - 2014

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dc.contributor.author Oke, Oji Okpusa
dc.date.accessioned 2021-01-27T03:34:01Z
dc.date.available 2021-01-27T03:34:01Z
dc.date.issued 2018-10
dc.identifier.uri http://hdl.handle.net/10352/449
dc.description M. Tech (Department of Accountancy, Faculty of Management Sciences) Vaal University of Technology. en_US
dc.description.abstract Numerous studies have been conducted to ascertain factors that impact on wealth creation of companies. It has been suggested by various researchers that economic value added (EVA) could be used to measure company wealth creation and a number of factors have been suggested that contribute to wealth creation for company shareholders. The purpose of this study is to determine the company characteristics that influence wealth creation. The study uses EVA, the dependent variable, as a measure of a company’s wealth creation. The company characteristics, independent variables, are operating capital size, capital gearing, export and domestic distribution market segments, sub-sectors and the type of product companies release into the market. Identifying company characteristics that influence wealth creation could enlighten investors on where capital should be directed in order to maximise wealth creation for the companies’ shareholders and the entire economy. Logistic regression analysis models were used to analyse 61 industrial companies listed on the Johannesburg Stock exchange (JSE) for the 10-year period of 2005 to 2014. The use of logistic regression for this analysis was necessitated by the binary nature of the data (EVA positive or negative) and logistic regression analysis is suitable for such binary data. A series of tests were conducted to assess the suitability of logistic regression analysis in evaluating the impact of company characteristics on EVA. The classification accuracy test, which shows the predictive accuracy or the forecast strength of the logistic regression model for this study yielded a forecast strength of the highest of 97.2 percent for 2006 and lowest of 63.2 percent for 2014. The results indicated the appropriateness of the logistic regression model for the study. The data on the EVA of companies were collected from INET-BFA. Other sets of data also obtained from INET-BFA include companies’ volume of operating capital, capital gearing, company product types, distribution channels and sub-sectors to which each company belongs. The historical inflation and exchange rates were also obtained and applied in comparing with EVA. The comparison was to determine if there was any relationship between EVA, exchange rates and inflation. Results of the logistic regression analysis model reveal that the sub-sector factor, capital size factor and capital gearing factor impact on EVA, while market segment and company product type do not impact on EVA. The results show that the sub-sector categories of manufacturing, retail and extraction have significant positive impact on EVA while property management does not impact on EVA. The large capital category of the capital size factor shows significant positive impact on EVA while the medium capital category shows a negative impact on EVA, leaving small capital size having no impact on EVA. The high as well as moderate capital gearing categories of the capital gearing factor show negative impact on EVA, while low gearing shows no impact on EVA. However, some years covered in the study did not have any significant factors. Results of wealth creation evaluation of the industrial companies using EVA as a metric reveals that the industrial companies created more value than was destroyed in terms of EVA. The results show that manufacturing, extraction and retail sub-sectors achieved net positive EVA, while the property management sub-sector achieved net EVA negative in the 10-year period. Furthermore, results of EVA comparison with foreign exchange and inflation rates indicated a relationship between EVA, exchange rate and rate of inflation. The results show that as inflation rises, foreign exchange depreciates, while EVA performance of companies drops during the same period. Findings and recommendations of this study are important to company managers as they offer crucial information regarding the types of activities organisations could engage in and for investors to consider the types of businesses in which to invest. The findings are also important in suggesting how companies could organise their capital structure as well as the size of the capital in order to optimise wealth creation. Such considerations by company managers and investors alike would help to increase wealth creation within the economic system. This study made use of five company characteristics, which were stated into various categories. Additional company characteristics should be used in a further study to identify other company attributes that may impact on EVA. There is also the need to carry out further studies using other methods to find out if different results could be achieved. In addition, a study is recommended to establish why no significant factor was identified in some of the years. en_US
dc.language.iso en en_US
dc.subject Economic value added, stakeholders, industrial, multi-functional organisations, intrinsic market value, company characteristics, logistic regression en_US
dc.subject.lcsh Johannesburg Stock Exchange. en_US
dc.subject.lcsh Stock exchanges -- South Africa. en_US
dc.subject.lcsh Investments -- South Africa. en_US
dc.title An Analysis: wealth creation by the industrial companies listed on the Johannesburg stock exchange of South Africa, 2005 - 2014 en_US
dc.type Thesis en_US
dc.contributor.supervisor Beneke, John
dc.contributor.co-supervisor Mudhombo, I.


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